\section{Introduction}
This report describes our participation in Knowledge Discovery and Data Mining (KDD) 2009 cup competition . The competition is a set of three separate binary classification problems, based on a large marketing database of Orange Telecom. The problems are churn, appetency and upselling. We tried variations of three alternate techniques to tackle the problem - Naive bayesian , Decision Trees  and Support Vector Machines. Section 2 describes the problems posed in further detail and Section 3 gives the evaluation criteria. In Section 4 we discuss the three approaches and the variations and Section 5 provides a discussion on the results. We propose future directions for our work in Section 6.
%\cite{nbayes},\cite{dtree},\cite{svm}




